In the drive for ongoing improvements in operating efficiency, industrial plants such as chemical plants, refineries, food processing plants, pharmaceutical plants, breweries, and other batch and continuous plant systems may employ computer-based modeling and simulation to optimize plant operations. These modeling systems are typically used to simulate plant processes by defining components and equipment of plants in computer models and then using mathematical computations to project or reveal the behavior of these systems as relevant parameters vary.
This type of modeling may be used to aid in the design and operation of such plants, as well as to provide computer-based training of operators by simulating plant and process responses to variations that can arise in real-world situations without the hazards or costs associated with subjecting plants to these events. In addition, predictions can be made about plant behavior in order to devise tactics for handling such events, should they occur. This type of modeling can also be used to assist in controlling plant operations by predicting system changes and responding accordingly by tying the information produced by the models into control loops of plant equipment.
Modeling of these systems typically involves iterative calculations of complex thermodynamic equations in order to accurately describe static views of dynamic systems. Given the rapidly changing state of these systems and the limitation of only being able to calculate discrete moments in time, this form of modeling can place heavy demands on a computer's central processing unit (CPU) as constant recalculations are required to keep the model updated. This heavy processing load challenges the ability to provide accurate data with sufficient speed to obtain predictive models in time to proactively forestall critical situations, thereby rendering plant control in a real world application difficult or impossible.